the questions still remains! Anyone? :)

Thank you for your response. I do not have much experience with imputing. I was thinking of using the mice package with predictive mean matching, which is able to create one imputed dataset by using complete(), which, as is my understanding, uses the first (of usually 5) imputed dataset.

Thank you! Unfortunate that I cannot redo it from Figure 1. But it makes sense to get the most accurate version of the model by the actual numerical outcome values. Thanks again!

Dear EdM, I started doubting again about the breakdown into subgroups, but this time for the calculation of the model. Under Table 3 it says that the Hazard Ratio and CI represent a 10-unit increase. Do you think that this means that they have regrouped their data in 10-unit subgroups (just as in Figure 1 - if so, I could recreate the model by using Figure 1). Or is this a multiplication you can do after you have made a model (which presents a 1-unit increase) (this seems unlikely to me)? Thanks again for your help!

Thanks again, will do!

I couldn't edit the previous post and the code had an error in it: Thank you! This makes a lot more sense now. I wanted to redo this with our own data, to see whether it is reproduceable. I understand how to make the model, but any tips on how to get the different categories and timepoints from Table 4 from predict()? This is my as far as I got with my current attempt (otherwise perhaps via survfit() and summary() ``` cox <- coxph(Surv(FU, event) ~ BWRS+ BPTA, data = db) predict(cox, newdata=db[BWRS==100 & dbBPTAT<=10,],type="survival",se=TRUE)```